18/04/2016 – Imports of counterfeit and pirated goods are worth nearly half a trillion dollars a year, or around 2.5% of global imports, with US, Italian and French brands the hardest hit and many of the proceeds going to organised crime, according to a new report by the OECD and the EU’s Intellectual Property Office.
“Trade in Counterfeit and Pirated Goods: Mapping the Economic Impact” puts the value of imported fake goods worldwide at USD 461 billion in 2013, compared with total imports in world trade of USD 17.9 trillion. Up to 5% of goods imported into the European Union are fakes. Most originate in middle income or emerging countries, with China the top producer.
The report analyses nearly half a million customs seizures around the world over 2011-13 to produce the most rigorous estimate to date of the scale of counterfeit trade. It points to a larger volume than a 2008 OECD study which estimated fake goods accounted for up to 1.9% of global imports, though the 2008 study used more limited data and methodology.
“The findings of this new report contradict the image that counterfeiters only hurt big companies and luxury goods manufacturers. They take advantage of our trust in trademarks and brand names to undermine economies and endanger lives,” said OECD Deputy Secretary-General Doug Frantz, launching the report with EUIPO Executive Director António Campinos as part of OECD Integrity Week.
Fake products crop up in everything from handbags and perfumes to machine parts and chemicals. Footwear is the most-copied item though trademarks are infringed even on strawberries and bananas. Counterfeiting also produces knockoffs that endanger lives – auto parts that fail, pharmaceuticals that make people sick, toys that harm children, baby formula that provides no nourishment and medical instruments that deliver false readings.\
The report covers all physical counterfeit goods, which infringe trademarks, design rights or patents, and tangible pirated products, which breach copyright. It does not cover online piracy, which is a further drain on the formal economy… http://www.oecd.org/industry/global-trade-in-fake-goods-worth-nearly-half-a-trillion-dollars-a-year.htm

SAS described machine learning in Machine Learning: What it is & why it matters:

Machine learning is a method of data analysis that automates analytical model building. Using algorithms that iteratively learn from data, machine learning allows computers to find hidden insights without being explicitly programmed where to look.
The iterative aspect of machine learning is important because as models are exposed to new data, they are able to independently adapt. They learn from previous computations to produce reliable, repeatable decisions and results. It’s a science that’s not new – but one that’s gaining fresh momentum.
Because of new computing technologies, machine learning today is not like machine learning of the past. While many machine learning algorithms have been around for a long time, the ability to automatically apply complex mathematical calculations to big data – over and over, faster and faster – is a recent development. Here are a few widely publicized examples of machine learning applications that you may be familiar with:
• The heavily hyped, self-driving Google car? The essence of machine learning.
• Online recommendation offers like those from Amazon and Netflix? Machine learning applications for everyday life.
• Knowing what customers are saying about you on Twitter? Machine learning combined with linguistic rule creation.
• Fraud detection? One of the more obvious, important uses in our world today.
Why the increased interest in machine learning?
Resurging interest in machine learning is due to the same factors that have made data mining and Bayesian analysis more popular than ever. Things like growing volumes and varieties of available data, computational processing that is cheaper and more powerful, and affordable data storage.
All of these things mean it’s possible to quickly and automatically produce models that can analyze bigger, more complex data and deliver faster, more accurate results – even on a very large scale. The result? High-value predictions that can guide better decisions and smart actions in real time without human intervention…. https://www.sas.com/en_id/insights/analytics/machine-learning.html

A team of researchers has developed a new mechanism that uses machine-learning algorithms to distinguish between genuine and counterfeit versions of the same product.
The work, led by New York University Professor Lakshminarayanan Subramanian, will be presented on Mon., Aug. 14 at the annual KDD Conference on Knowledge Discovery and Data Mining in Halifax, Nova Scotia….
The system described in the presentation is commercialized by Entrupy Inc., an NYU startup founded by Ashlesh Sharma, a doctoral graduate from the Courant Institute, Vidyuth Srinivasan, and Subramanian.
Counterfeit goods represent a massive worldwide problem with nearly every high-valued physical object or product directly affected by this issue, the researchers note. Some reports indicate counterfeit trafficking represents 7 percent of the world’s trade today.
While other counterfeit-detection methods exist, these are invasive and run the risk of damaging the products under examination.
The Entrupy method, by contrast, provides a non-intrusive solution to easily distinguish authentic versions of the product produced by the original manufacturer and fake versions of the product produced by counterfeiters….
“The classification accuracy is more than 98 percent, and we show how our system works with a cellphone to verify the authenticity of everyday objects,” notes Subramanian.
A demo of the technology may be viewed here: https://www.youtube.com/watch?v=DsdsY8-gljg (courtesy of Entrupy Inc.)
To date, Entrupy, which recently received $2.6 million in funding from a team of investors, has authenticated $14 million worth of goods.

Citation:

Machine learning helps spot counterfeit consumer products
Date: August 11, 2017
Source: New York University
Summary:
A team of researchers has developed a new mechanism that uses machine-learning algorithms to distinguish between genuine and counterfeit versions of the same product.

Here is the NYU press release:

News Release
Researchers Use Machine Learning to Spot Counterfeit Consumer Products
________________________________________
Aug 11, 2017
Engineering, Science and Technology Research Courant Institute of Mathematical Sciences Faculty
New York City
A team of researchers has developed a new mechanism that uses machine-learning algorithms to distinguish between genuine and counterfeit versions of the same product.

A team of researchers has developed a new mechanism that uses machine-learning algorithms to distinguish between genuine and counterfeit versions of the same product. Image courtesy of Entrupy, Inc.
A team of researchers has developed a new mechanism that uses machine-learning algorithms to distinguish between genuine and counterfeit versions of the same product.

The work, led by New York University Professor Lakshminarayanan Subramanian, will be presented on Mon., Aug. 14 at the annual KDD Conference on Knowledge Discovery and Data Mining in Halifax, Nova Scotia.
“The underlying principle of our system stems from the idea that microscopic characteristics in a genuine product or a class of products—corresponding to the same larger product line—exhibit inherent similarities that can be used to distinguish these products from their corresponding counterfeit versions,” explains Subramanian, a professor at NYU’s Courant Institute of Mathematical Sciences.
The system described in the presentation is commercialized by Entrupy Inc., an NYU startup founded by Ashlesh Sharma, a doctoral graduate from the Courant Institute, Vidyuth Srinivasan, and Subramanian.
Counterfeit goods represent a massive worldwide problem with nearly every high-valued physical object or product directly affected by this issue, the researchers note. Some reports indicate counterfeit trafficking represents 7 percent of the world’s trade today.
While other counterfeit-detection methods exist, these are invasive and run the risk of damaging the products under examination.
The Entrupy method, by contrast, provides a non-intrusive solution to easily distinguish authentic versions of the product produced by the original manufacturer and fake versions of the product produced by counterfeiters.
It does so by deploying a dataset of three million images across various objects and materials such as fabrics, leather, pills, electronics, toys and shoes.
“The classification accuracy is more than 98 percent, and we show how our system works with a cellphone to verify the authenticity of everyday objects,” notes Subramanian.
A demo of the technology may be viewed here (courtesy of Entrupy Inc.).
To date, Entrupy, which recently received $2.6 million in funding from a team of investors, has authenticated $14 million worth of goods.
For a copy of the paper, “The Fake vs Real Goods Problem: Microscopy and Machine Learning to the Rescue,” please contact James Devitt, NYU’s Office of Public Affairs, at 212.998.6808 or james.devitt@nyu.edu.

Press Contact
James Devitt
James Devitt
(212) 998-6808

Employment opportunities in machine learning are expected to increase.

UDACITY described machine learning employment opportunities in :5 Skills You Need to Become a Machine Learning Engineer:

To begin, there are two very important things that you should understand if you’re considering a career as a Machine Learning engineer. First, it’s not a “pure” academic role. You don’t necessarily have to have a research or academic background. Second, it’s not enough to have either software engineering or data science experience. You ideally need both.
Data Analyst vs. Machine Learning Engineer
It’s also critical to understand the differences between a Data Analyst and a Machine Learning engineer. In simplest form, the key distinction has to do with the end goal. As a Data Analyst, you’re analyzing data in order to tell a story, and to produce actionable insights. The emphasis is on dissemination—charts, models, visualizations. The analysis is performed and presented by human beings, to other human beings who may then go on to make business decisions based on what’s been presented. This is especially important to note—the “audience” for your output is human. As a Machine Learning engineer, on the other hand, your final “output” is working software (not the analyses or visualizations that you may have to create along the way), and your “audience” for this output often consists of other software components that run autonomously with minimal human supervision. The intelligence is still meant to be actionable, but in the Machine Learning model, the decisions are being made by machines and they affect how a product or service behaves. This is why the software engineering skill set is so important to a career in Machine Learning.
Understanding The Ecosystem
Before getting into specific skills, there is one more concept to address. Being a Machine Learning engineer necessitates understanding the entire ecosystem that you’re designing for.
Let’s say you’re working for a grocery chain, and the company wants to start issuing targeted coupons based on things like the past purchase history of customers, with a goal of generating coupons that shoppers will actually use. In a Data Analysis model, you could collect the purchase data, do the analysis to figure out trends, and then propose strategies. The Machine Learning approach would be to write an automated coupon generation system. But what does it take to write that system, and have it work? You have to understand the whole ecosystem—inventory, catalog, pricing, purchase orders, bill generation, Point of Sale software, CRM software, etc.
Ultimately, the process is less about understanding Machine Learning algorithms—or when and how to apply them—and more about understanding the systemic interrelationships, and writing working software that will successfully integrate and interface. Remember, Machine Learning output is actually working software! http://blog.udacity.com/2016/04/5-skills-you-need-to-become-a-machine-learning-engineer.html

Education guidance counselors should be informed about opportunities in machine learning.

Moi discussed preschool education in The state of preschool education is dire:

Preschool is a portal to the continuum of life long learning. A good preschool stimulates the learning process and prompts the child into asking questions about their world and environment. Baby Centeroffers advice about how to find a good preschool and general advice to expectant parents. At the core of why education is important is the goal of equipping every child with the knowledge and skills to pursue THEIR dream, whatever that dream is. Christine Armario and Dorie Turner are reporting in the AP article, AP News Break: Nearly 1 in 4 Fails Military Exam which appeared in the Seattle Times:

Nearly one-fourth of the students who try to join the U.S. Army fail its entrance exam, painting a grim picture of an education system that produces graduates who can’t answer basic math, science and reading questions, according to a new study released Tuesday.

Lesli A. Maxwell reports in the Education Week article, Study Finds U.S. Trailing in Preschool Enrollment a new study by the Organization for Economic Cooperation and Development (OECD):

According to the Paris-based OECD’s “Education at a Glance 2012,” a report released today, the United States ranks 28th out of 38 countries for the share of 4-year-olds enrolled in pre-primary education programs, at 69 percent. That’s compared with more than 95 percent enrollment rates in France, the Netherlands, Spain, and Mexico, which lead the world in early-childhood participation rates for 4-year-olds. Ireland, Poland, Finland, and Brazil are among the nations that trail the United States.

The United States also invests significantly less public money in early-childhood programs than its counterparts in the Group of Twenty, or G-20, economies, which include 19 countries and the European Union. On average, across the countries that are compared in the OECD report, 84 percent of early-childhood students were enrolled in public programs or in private settings that receive major government resources in 2010. In this country, just 55 percent of early-childhood students were enrolled in publicly supported programs in 2010, while 45 percent attended independent private programs.

Young women are five percentage points more likely than young men to become better educated than their parents (40% compared with 35%), while young men are more likely than young women to have lower educational attainment than their parents (15% compared with 11%).

The educational attainment of mothers has a stronger impact on students’ reading performance than the primary language at home or the proportion of immigrant students in a school.

Across OECD countries, more than one-third of immigrant students attend schools with the highest concentrations of students with low-educated mothers. In the European Union, more than half do.

The share of private funding for tertiary education increased between 2000 and 2009 in 18 out of 25 countries. The share increased by 5 percentage points on average, and by more than 12 percentage points in the Slovak Republic (from 8.8% to 30%) and the United Kingdom (from 32.3% to 70.4%).

An increasing number of OECD countries are charging higher tuition fees for international students than for national students, and many also differentiate tuition fees by field of education, largely because of the difference in the public cost of studies.

Between 2000 and 2009, in 24 of the 29 countries for which data are available, expenditure per primary, secondary and post-secondary non-tertiary student increased spending by at least 16%. The increase exceeded 50% in Brazil, the Czech Republic, Estonia, Hungary, Ireland, Korea, Poland, the Slovak Republic and the United Kingdom. By contrast, in France, Israel and Italy, this expenditure increased by only 10% or less between 2000 and 2009.

School environment

Salaries for teachers with at least 15 years of experience average USD 35 630 at the pre-primary level, USD 37 603 at the primary level, USD 39 401 at the lower secondary level and USD 41 182 at the upper secondary level.

Teachers’ salaries increased in real terms in most countries between 2000 and 2010. In Denmark, Estonia, Ireland, Portugal and Scotland, salaries increased by at least 20%. In the Czech Republic (primary and lower secondary levels) and in Turkey, salaries doubled over the past decade. Only in France and Japan did teachers’ salaries decrease in real terms, by more than 5%.

The number of teaching hours per teacher in public schools in 2010 averages 782 hours per year in primary education, 704 hours in lower secondary education, and 658 hours in upper secondary education. This is little changed from 2000 but has changed dramatically in a few countries. It increased by more than 25% in the Czech Republic at the primary level and in Portugal and Spain at the secondary level.

Some two-thirds of teachers and academic staff are women on average in the OECD, but the proportion of female teachers decreases as the level of education increases: ranging from 97% at pre-primary to 41% at tertiary level.